Overview

Dataset statistics

Number of variables24
Number of observations25000
Missing cells13779
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 MiB
Average record size in memory192.0 B

Variable types

Categorical12
Numeric12

Alerts

Ware_house_ID has a high cardinality: 25000 distinct valuesHigh cardinality
WH_Manager_ID has a high cardinality: 25000 distinct valuesHigh cardinality
wh_est_year is highly overall correlated with storage_issue_reported_l3m and 1 other fieldsHigh correlation
storage_issue_reported_l3m is highly overall correlated with wh_est_year and 1 other fieldsHigh correlation
govt_check_l3m is highly overall correlated with WH_capacity_size and 1 other fieldsHigh correlation
product_wg_ton is highly overall correlated with wh_est_year and 1 other fieldsHigh correlation
WH_capacity_size is highly overall correlated with govt_check_l3m and 1 other fieldsHigh correlation
WH_regional_zone is highly overall correlated with govt_check_l3m and 1 other fieldsHigh correlation
Location_type is highly imbalanced (59.2%)Imbalance
flood_impacted is highly imbalanced (53.7%)Imbalance
flood_proof is highly imbalanced (69.4%)Imbalance
workers_num has 990 (4.0%) missing valuesMissing
wh_est_year has 11881 (47.5%) missing valuesMissing
approved_wh_govt_certificate has 908 (3.6%) missing valuesMissing
Ware_house_ID is uniformly distributedUniform
WH_Manager_ID is uniformly distributedUniform
Ware_house_ID has unique valuesUnique
WH_Manager_ID has unique valuesUnique
num_refill_req_l3m has 2912 (11.6%) zerosZeros
transport_issue_l1y has 15215 (60.9%) zerosZeros
storage_issue_reported_l3m has 908 (3.6%) zerosZeros
wh_breakdown_l3m has 908 (3.6%) zerosZeros

Reproduction

Analysis started2023-03-17 09:16:20.133851
Analysis finished2023-03-17 09:16:38.909494
Duration18.78 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Ware_house_ID
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct25000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
WH_100000
 
1
WH_116650
 
1
WH_116672
 
1
WH_116671
 
1
WH_116670
 
1
Other values (24995)
24995 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters225000
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25000 ?
Unique (%)100.0%

Sample

1st rowWH_100000
2nd rowWH_100001
3rd rowWH_100002
4th rowWH_100003
5th rowWH_100004

Common Values

ValueCountFrequency (%)
WH_100000 1
 
< 0.1%
WH_116650 1
 
< 0.1%
WH_116672 1
 
< 0.1%
WH_116671 1
 
< 0.1%
WH_116670 1
 
< 0.1%
WH_116669 1
 
< 0.1%
WH_116668 1
 
< 0.1%
WH_116667 1
 
< 0.1%
WH_116666 1
 
< 0.1%
WH_116665 1
 
< 0.1%
Other values (24990) 24990
> 99.9%

Length

2023-03-17T14:46:39.032717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wh_100000 1
 
< 0.1%
wh_100076 1
 
< 0.1%
wh_100005 1
 
< 0.1%
wh_100006 1
 
< 0.1%
wh_100007 1
 
< 0.1%
wh_100008 1
 
< 0.1%
wh_100009 1
 
< 0.1%
wh_100010 1
 
< 0.1%
wh_100011 1
 
< 0.1%
wh_100012 1
 
< 0.1%
Other values (24990) 24990
> 99.9%

Most occurring characters

ValueCountFrequency (%)
1 45500
20.2%
W 25000
11.1%
H 25000
11.1%
_ 25000
11.1%
0 20500
9.1%
2 15500
 
6.9%
4 10500
 
4.7%
3 10500
 
4.7%
6 9500
 
4.2%
5 9500
 
4.2%
Other values (3) 28500
12.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 150000
66.7%
Uppercase Letter 50000
 
22.2%
Connector Punctuation 25000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 45500
30.3%
0 20500
13.7%
2 15500
 
10.3%
4 10500
 
7.0%
3 10500
 
7.0%
6 9500
 
6.3%
5 9500
 
6.3%
7 9500
 
6.3%
9 9500
 
6.3%
8 9500
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
W 25000
50.0%
H 25000
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 25000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 175000
77.8%
Latin 50000
 
22.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 45500
26.0%
_ 25000
14.3%
0 20500
11.7%
2 15500
 
8.9%
4 10500
 
6.0%
3 10500
 
6.0%
6 9500
 
5.4%
5 9500
 
5.4%
7 9500
 
5.4%
9 9500
 
5.4%
Latin
ValueCountFrequency (%)
W 25000
50.0%
H 25000
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 225000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 45500
20.2%
W 25000
11.1%
H 25000
11.1%
_ 25000
11.1%
0 20500
9.1%
2 15500
 
6.9%
4 10500
 
4.7%
3 10500
 
4.7%
6 9500
 
4.2%
5 9500
 
4.2%
Other values (3) 28500
12.7%

WH_Manager_ID
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct25000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
EID_50000
 
1
EID_66650
 
1
EID_66672
 
1
EID_66671
 
1
EID_66670
 
1
Other values (24995)
24995 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters225000
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25000 ?
Unique (%)100.0%

Sample

1st rowEID_50000
2nd rowEID_50001
3rd rowEID_50002
4th rowEID_50003
5th rowEID_50004

Common Values

ValueCountFrequency (%)
EID_50000 1
 
< 0.1%
EID_66650 1
 
< 0.1%
EID_66672 1
 
< 0.1%
EID_66671 1
 
< 0.1%
EID_66670 1
 
< 0.1%
EID_66669 1
 
< 0.1%
EID_66668 1
 
< 0.1%
EID_66667 1
 
< 0.1%
EID_66666 1
 
< 0.1%
EID_66665 1
 
< 0.1%
Other values (24990) 24990
> 99.9%

Length

2023-03-17T14:46:39.129028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
eid_50000 1
 
< 0.1%
eid_50076 1
 
< 0.1%
eid_50005 1
 
< 0.1%
eid_50006 1
 
< 0.1%
eid_50007 1
 
< 0.1%
eid_50008 1
 
< 0.1%
eid_50009 1
 
< 0.1%
eid_50010 1
 
< 0.1%
eid_50011 1
 
< 0.1%
eid_50012 1
 
< 0.1%
Other values (24990) 24990
> 99.9%

Most occurring characters

ValueCountFrequency (%)
E 25000
11.1%
I 25000
11.1%
D 25000
11.1%
_ 25000
11.1%
5 19500
8.7%
6 19500
8.7%
7 14500
 
6.4%
0 10500
 
4.7%
2 10500
 
4.7%
1 10500
 
4.7%
Other values (4) 40000
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125000
55.6%
Uppercase Letter 75000
33.3%
Connector Punctuation 25000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 19500
15.6%
6 19500
15.6%
7 14500
11.6%
0 10500
8.4%
2 10500
8.4%
1 10500
8.4%
4 10500
8.4%
3 10500
8.4%
9 9500
7.6%
8 9500
7.6%
Uppercase Letter
ValueCountFrequency (%)
E 25000
33.3%
I 25000
33.3%
D 25000
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 25000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 150000
66.7%
Latin 75000
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 25000
16.7%
5 19500
13.0%
6 19500
13.0%
7 14500
9.7%
0 10500
7.0%
2 10500
7.0%
1 10500
7.0%
4 10500
7.0%
3 10500
7.0%
9 9500
 
6.3%
Latin
ValueCountFrequency (%)
E 25000
33.3%
I 25000
33.3%
D 25000
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 225000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 25000
11.1%
I 25000
11.1%
D 25000
11.1%
_ 25000
11.1%
5 19500
8.7%
6 19500
8.7%
7 14500
 
6.4%
0 10500
 
4.7%
2 10500
 
4.7%
1 10500
 
4.7%
Other values (4) 40000
17.8%

Location_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
Rural
22957 
Urban
 
2043

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters125000
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowRural
3rd rowRural
4th rowRural
5th rowRural

Common Values

ValueCountFrequency (%)
Rural 22957
91.8%
Urban 2043
 
8.2%

Length

2023-03-17T14:46:39.209001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T14:46:39.294150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
rural 22957
91.8%
urban 2043
 
8.2%

Most occurring characters

ValueCountFrequency (%)
r 25000
20.0%
a 25000
20.0%
R 22957
18.4%
u 22957
18.4%
l 22957
18.4%
U 2043
 
1.6%
b 2043
 
1.6%
n 2043
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 100000
80.0%
Uppercase Letter 25000
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 25000
25.0%
a 25000
25.0%
u 22957
23.0%
l 22957
23.0%
b 2043
 
2.0%
n 2043
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
R 22957
91.8%
U 2043
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 125000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 25000
20.0%
a 25000
20.0%
R 22957
18.4%
u 22957
18.4%
l 22957
18.4%
U 2043
 
1.6%
b 2043
 
1.6%
n 2043
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 25000
20.0%
a 25000
20.0%
R 22957
18.4%
u 22957
18.4%
l 22957
18.4%
U 2043
 
1.6%
b 2043
 
1.6%
n 2043
 
1.6%

WH_capacity_size
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
Large
10169 
Mid
10020 
Small
4811 

Length

Max length5
Median length5
Mean length4.1984
Min length3

Characters and Unicode

Total characters104960
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSmall
2nd rowLarge
3rd rowMid
4th rowMid
5th rowLarge

Common Values

ValueCountFrequency (%)
Large 10169
40.7%
Mid 10020
40.1%
Small 4811
19.2%

Length

2023-03-17T14:46:39.367081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T14:46:39.469220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
large 10169
40.7%
mid 10020
40.1%
small 4811
19.2%

Most occurring characters

ValueCountFrequency (%)
a 14980
14.3%
L 10169
9.7%
r 10169
9.7%
g 10169
9.7%
e 10169
9.7%
M 10020
9.5%
i 10020
9.5%
d 10020
9.5%
l 9622
9.2%
S 4811
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 79960
76.2%
Uppercase Letter 25000
 
23.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 14980
18.7%
r 10169
12.7%
g 10169
12.7%
e 10169
12.7%
i 10020
12.5%
d 10020
12.5%
l 9622
12.0%
m 4811
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
L 10169
40.7%
M 10020
40.1%
S 4811
19.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 104960
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 14980
14.3%
L 10169
9.7%
r 10169
9.7%
g 10169
9.7%
e 10169
9.7%
M 10020
9.5%
i 10020
9.5%
d 10020
9.5%
l 9622
9.2%
S 4811
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 14980
14.3%
L 10169
9.7%
r 10169
9.7%
g 10169
9.7%
e 10169
9.7%
M 10020
9.5%
i 10020
9.5%
d 10020
9.5%
l 9622
9.2%
S 4811
 
4.6%

zone
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
North
10278 
West
7931 
South
6362 
East
 
429

Length

Max length5
Median length5
Mean length4.6656
Min length4

Characters and Unicode

Total characters116640
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWest
2nd rowNorth
3rd rowSouth
4th rowNorth
5th rowNorth

Common Values

ValueCountFrequency (%)
North 10278
41.1%
West 7931
31.7%
South 6362
25.4%
East 429
 
1.7%

Length

2023-03-17T14:46:39.560321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T14:46:39.657120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
north 10278
41.1%
west 7931
31.7%
south 6362
25.4%
east 429
 
1.7%

Most occurring characters

ValueCountFrequency (%)
t 25000
21.4%
o 16640
14.3%
h 16640
14.3%
N 10278
8.8%
r 10278
8.8%
s 8360
 
7.2%
W 7931
 
6.8%
e 7931
 
6.8%
S 6362
 
5.5%
u 6362
 
5.5%
Other values (2) 858
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 91640
78.6%
Uppercase Letter 25000
 
21.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 25000
27.3%
o 16640
18.2%
h 16640
18.2%
r 10278
11.2%
s 8360
 
9.1%
e 7931
 
8.7%
u 6362
 
6.9%
a 429
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
N 10278
41.1%
W 7931
31.7%
S 6362
25.4%
E 429
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 116640
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 25000
21.4%
o 16640
14.3%
h 16640
14.3%
N 10278
8.8%
r 10278
8.8%
s 8360
 
7.2%
W 7931
 
6.8%
e 7931
 
6.8%
S 6362
 
5.5%
u 6362
 
5.5%
Other values (2) 858
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 25000
21.4%
o 16640
14.3%
h 16640
14.3%
N 10278
8.8%
r 10278
8.8%
s 8360
 
7.2%
W 7931
 
6.8%
e 7931
 
6.8%
S 6362
 
5.5%
u 6362
 
5.5%
Other values (2) 858
 
0.7%

WH_regional_zone
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
Zone 6
8339 
Zone 5
4587 
Zone 4
4176 
Zone 2
2963 
Zone 3
2881 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters150000
Distinct characters11
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZone 6
2nd rowZone 5
3rd rowZone 2
4th rowZone 3
5th rowZone 5

Common Values

ValueCountFrequency (%)
Zone 6 8339
33.4%
Zone 5 4587
18.3%
Zone 4 4176
16.7%
Zone 2 2963
 
11.9%
Zone 3 2881
 
11.5%
Zone 1 2054
 
8.2%

Length

2023-03-17T14:46:39.735022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T14:46:39.831813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
zone 25000
50.0%
6 8339
 
16.7%
5 4587
 
9.2%
4 4176
 
8.4%
2 2963
 
5.9%
3 2881
 
5.8%
1 2054
 
4.1%

Most occurring characters

ValueCountFrequency (%)
Z 25000
16.7%
o 25000
16.7%
n 25000
16.7%
e 25000
16.7%
25000
16.7%
6 8339
 
5.6%
5 4587
 
3.1%
4 4176
 
2.8%
2 2963
 
2.0%
3 2881
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 75000
50.0%
Uppercase Letter 25000
 
16.7%
Space Separator 25000
 
16.7%
Decimal Number 25000
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 8339
33.4%
5 4587
18.3%
4 4176
16.7%
2 2963
 
11.9%
3 2881
 
11.5%
1 2054
 
8.2%
Lowercase Letter
ValueCountFrequency (%)
o 25000
33.3%
n 25000
33.3%
e 25000
33.3%
Uppercase Letter
ValueCountFrequency (%)
Z 25000
100.0%
Space Separator
ValueCountFrequency (%)
25000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 100000
66.7%
Common 50000
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
25000
50.0%
6 8339
 
16.7%
5 4587
 
9.2%
4 4176
 
8.4%
2 2963
 
5.9%
3 2881
 
5.8%
1 2054
 
4.1%
Latin
ValueCountFrequency (%)
Z 25000
25.0%
o 25000
25.0%
n 25000
25.0%
e 25000
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Z 25000
16.7%
o 25000
16.7%
n 25000
16.7%
e 25000
16.7%
25000
16.7%
6 8339
 
5.6%
5 4587
 
3.1%
4 4176
 
2.8%
2 2963
 
2.0%
3 2881
 
1.9%

num_refill_req_l3m
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.08904
Minimum0
Maximum8
Zeros2912
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-03-17T14:46:39.942480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6066115
Coefficient of variation (CV)0.63746296
Kurtosis-1.2206972
Mean4.08904
Median Absolute Deviation (MAD)2
Skewness-0.075216703
Sum102226
Variance6.7944237
MonotonicityNot monotonic
2023-03-17T14:46:40.019636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 2990
12.0%
8 2970
11.9%
5 2941
11.8%
0 2912
11.6%
7 2873
11.5%
1 2856
11.4%
4 2846
11.4%
6 2804
11.2%
2 1808
7.2%
ValueCountFrequency (%)
0 2912
11.6%
1 2856
11.4%
2 1808
7.2%
3 2990
12.0%
4 2846
11.4%
5 2941
11.8%
6 2804
11.2%
7 2873
11.5%
8 2970
11.9%
ValueCountFrequency (%)
8 2970
11.9%
7 2873
11.5%
6 2804
11.2%
5 2941
11.8%
4 2846
11.4%
3 2990
12.0%
2 1808
7.2%
1 2856
11.4%
0 2912
11.6%

transport_issue_l1y
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77368
Minimum0
Maximum5
Zeros15215
Zeros (%)60.9%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-03-17T14:46:40.097741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1994485
Coefficient of variation (CV)1.5503161
Kurtosis1.8384391
Mean0.77368
Median Absolute Deviation (MAD)0
Skewness1.6109066
Sum19342
Variance1.4386768
MonotonicityNot monotonic
2023-03-17T14:46:40.164528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 15215
60.9%
1 4644
 
18.6%
2 2198
 
8.8%
3 1818
 
7.3%
4 777
 
3.1%
5 348
 
1.4%
ValueCountFrequency (%)
0 15215
60.9%
1 4644
 
18.6%
2 2198
 
8.8%
3 1818
 
7.3%
4 777
 
3.1%
5 348
 
1.4%
ValueCountFrequency (%)
5 348
 
1.4%
4 777
 
3.1%
3 1818
 
7.3%
2 2198
 
8.8%
1 4644
 
18.6%
0 15215
60.9%

Competitor_in_mkt
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1042
Minimum0
Maximum12
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-03-17T14:46:40.262279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.141663
Coefficient of variation (CV)0.36778012
Kurtosis1.7863684
Mean3.1042
Median Absolute Deviation (MAD)1
Skewness0.97845565
Sum77605
Variance1.3033945
MonotonicityNot monotonic
2023-03-17T14:46:40.345394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 8669
34.7%
3 7094
28.4%
4 6708
26.8%
5 1265
 
5.1%
6 546
 
2.2%
1 432
 
1.7%
7 189
 
0.8%
8 76
 
0.3%
9 13
 
0.1%
10 6
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 432
 
1.7%
2 8669
34.7%
3 7094
28.4%
4 6708
26.8%
5 1265
 
5.1%
6 546
 
2.2%
7 189
 
0.8%
8 76
 
0.3%
9 13
 
0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
10 6
 
< 0.1%
9 13
 
0.1%
8 76
 
0.3%
7 189
 
0.8%
6 546
 
2.2%
5 1265
 
5.1%
4 6708
26.8%
3 7094
28.4%
2 8669
34.7%

retail_shop_num
Real number (ℝ)

Distinct4906
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4985.7116
Minimum1821
Maximum11008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-03-17T14:46:40.431004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1821
5-th percentile3513
Q14313
median4859
Q35500
95-th percentile6934.1
Maximum11008
Range9187
Interquartile range (IQR)1187

Descriptive statistics

Standard deviation1052.8253
Coefficient of variation (CV)0.2111685
Kurtosis1.851946
Mean4985.7116
Median Absolute Deviation (MAD)587
Skewness0.90830174
Sum1.2464279 × 108
Variance1108441
MonotonicityNot monotonic
2023-03-17T14:46:40.540351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4860 22
 
0.1%
4808 22
 
0.1%
4367 21
 
0.1%
4816 21
 
0.1%
4659 21
 
0.1%
4611 21
 
0.1%
5022 21
 
0.1%
4918 20
 
0.1%
4783 20
 
0.1%
4850 20
 
0.1%
Other values (4896) 24791
99.2%
ValueCountFrequency (%)
1821 1
< 0.1%
1871 1
< 0.1%
1905 1
< 0.1%
1915 1
< 0.1%
1953 1
< 0.1%
1959 1
< 0.1%
1971 1
< 0.1%
1980 1
< 0.1%
1999 1
< 0.1%
2008 1
< 0.1%
ValueCountFrequency (%)
11008 1
< 0.1%
10846 1
< 0.1%
10562 1
< 0.1%
10320 1
< 0.1%
10224 1
< 0.1%
10169 1
< 0.1%
10156 1
< 0.1%
10151 1
< 0.1%
10150 1
< 0.1%
10041 1
< 0.1%

wh_owner_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
Company Owned
13578 
Rented
11422 

Length

Max length13
Median length13
Mean length9.80184
Min length6

Characters and Unicode

Total characters245046
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRented
2nd rowCompany Owned
3rd rowCompany Owned
4th rowRented
5th rowCompany Owned

Common Values

ValueCountFrequency (%)
Company Owned 13578
54.3%
Rented 11422
45.7%

Length

2023-03-17T14:46:40.649701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T14:46:40.727806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
company 13578
35.2%
owned 13578
35.2%
rented 11422
29.6%

Most occurring characters

ValueCountFrequency (%)
n 38578
15.7%
e 36422
14.9%
d 25000
10.2%
C 13578
 
5.5%
o 13578
 
5.5%
m 13578
 
5.5%
p 13578
 
5.5%
a 13578
 
5.5%
y 13578
 
5.5%
13578
 
5.5%
Other values (4) 50000
20.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 192890
78.7%
Uppercase Letter 38578
 
15.7%
Space Separator 13578
 
5.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 38578
20.0%
e 36422
18.9%
d 25000
13.0%
o 13578
 
7.0%
m 13578
 
7.0%
p 13578
 
7.0%
a 13578
 
7.0%
y 13578
 
7.0%
w 13578
 
7.0%
t 11422
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
C 13578
35.2%
O 13578
35.2%
R 11422
29.6%
Space Separator
ValueCountFrequency (%)
13578
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 231468
94.5%
Common 13578
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 38578
16.7%
e 36422
15.7%
d 25000
10.8%
C 13578
 
5.9%
o 13578
 
5.9%
m 13578
 
5.9%
p 13578
 
5.9%
a 13578
 
5.9%
y 13578
 
5.9%
O 13578
 
5.9%
Other values (3) 36422
15.7%
Common
ValueCountFrequency (%)
13578
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 245046
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 38578
15.7%
e 36422
14.9%
d 25000
10.2%
C 13578
 
5.5%
o 13578
 
5.5%
m 13578
 
5.5%
p 13578
 
5.5%
a 13578
 
5.5%
y 13578
 
5.5%
13578
 
5.5%
Other values (4) 50000
20.4%

distributor_num
Real number (ℝ)

Distinct56
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.41812
Minimum15
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-03-17T14:46:40.824330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile17
Q129
median42
Q356
95-th percentile68
Maximum70
Range55
Interquartile range (IQR)27

Descriptive statistics

Standard deviation16.064329
Coefficient of variation (CV)0.37871383
Kurtosis-1.1875636
Mean42.41812
Median Absolute Deviation (MAD)14
Skewness0.015212662
Sum1060453
Variance258.06266
MonotonicityNot monotonic
2023-03-17T14:46:40.934194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 490
 
2.0%
41 481
 
1.9%
69 481
 
1.9%
37 479
 
1.9%
29 479
 
1.9%
21 478
 
1.9%
40 477
 
1.9%
28 474
 
1.9%
47 469
 
1.9%
33 467
 
1.9%
Other values (46) 20225
80.9%
ValueCountFrequency (%)
15 436
1.7%
16 431
1.7%
17 415
1.7%
18 439
1.8%
19 416
1.7%
20 440
1.8%
21 478
1.9%
22 460
1.8%
23 450
1.8%
24 454
1.8%
ValueCountFrequency (%)
70 438
1.8%
69 481
1.9%
68 400
1.6%
67 422
1.7%
66 421
1.7%
65 453
1.8%
64 452
1.8%
63 459
1.8%
62 447
1.8%
61 444
1.8%

flood_impacted
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
0
22546 
1
2454 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 22546
90.2%
1 2454
 
9.8%

Length

2023-03-17T14:46:41.028136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T14:46:41.106247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22546
90.2%
1 2454
 
9.8%

Most occurring characters

ValueCountFrequency (%)
0 22546
90.2%
1 2454
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22546
90.2%
1 2454
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
Common 25000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22546
90.2%
1 2454
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22546
90.2%
1 2454
 
9.8%

flood_proof
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
0
23634 
1
 
1366

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23634
94.5%
1 1366
 
5.5%

Length

2023-03-17T14:46:41.168732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T14:46:41.250299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 23634
94.5%
1 1366
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 23634
94.5%
1 1366
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23634
94.5%
1 1366
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 25000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23634
94.5%
1 1366
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23634
94.5%
1 1366
 
5.5%

electric_supply
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
1
16422 
0
8578 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 16422
65.7%
0 8578
34.3%

Length

2023-03-17T14:46:41.304603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T14:46:41.382709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 16422
65.7%
0 8578
34.3%

Most occurring characters

ValueCountFrequency (%)
1 16422
65.7%
0 8578
34.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16422
65.7%
0 8578
34.3%

Most occurring scripts

ValueCountFrequency (%)
Common 25000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 16422
65.7%
0 8578
34.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 16422
65.7%
0 8578
34.3%

dist_from_hub
Real number (ℝ)

Distinct217
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean163.53732
Minimum55
Maximum271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-03-17T14:46:41.476434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile65
Q1109
median164
Q3218
95-th percentile261
Maximum271
Range216
Interquartile range (IQR)109

Descriptive statistics

Standard deviation62.718609
Coefficient of variation (CV)0.38351252
Kurtosis-1.2006823
Mean163.53732
Median Absolute Deviation (MAD)54
Skewness-0.005998691
Sum4088433
Variance3933.624
MonotonicityNot monotonic
2023-03-17T14:46:41.570164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
239 144
 
0.6%
84 142
 
0.6%
209 140
 
0.6%
258 140
 
0.6%
204 140
 
0.6%
242 138
 
0.6%
145 138
 
0.6%
256 137
 
0.5%
186 136
 
0.5%
108 135
 
0.5%
Other values (207) 23610
94.4%
ValueCountFrequency (%)
55 104
0.4%
56 111
0.4%
57 126
0.5%
58 122
0.5%
59 113
0.5%
60 107
0.4%
61 112
0.4%
62 118
0.5%
63 128
0.5%
64 100
0.4%
ValueCountFrequency (%)
271 130
0.5%
270 129
0.5%
269 125
0.5%
268 121
0.5%
267 123
0.5%
266 103
0.4%
265 116
0.5%
264 103
0.4%
263 103
0.4%
262 107
0.4%

workers_num
Real number (ℝ)

Distinct60
Distinct (%)0.2%
Missing990
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean28.944398
Minimum10
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-03-17T14:46:41.898212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile18
Q124
median28
Q333
95-th percentile43
Maximum98
Range88
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.872534
Coefficient of variation (CV)0.27198817
Kurtosis3.4093352
Mean28.944398
Median Absolute Deviation (MAD)5
Skewness1.0599106
Sum694955
Variance61.976791
MonotonicityNot monotonic
2023-03-17T14:46:41.991942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 1491
 
6.0%
27 1447
 
5.8%
29 1402
 
5.6%
26 1290
 
5.2%
25 1268
 
5.1%
24 1228
 
4.9%
30 1202
 
4.8%
31 1132
 
4.5%
23 1077
 
4.3%
32 1077
 
4.3%
Other values (50) 11396
45.6%
(Missing) 990
 
4.0%
ValueCountFrequency (%)
10 5
 
< 0.1%
11 5
 
< 0.1%
12 15
 
0.1%
13 24
 
0.1%
14 104
 
0.4%
15 155
 
0.6%
16 328
1.3%
17 445
1.8%
18 559
2.2%
19 590
2.4%
ValueCountFrequency (%)
98 5
 
< 0.1%
92 5
 
< 0.1%
78 5
 
< 0.1%
72 5
 
< 0.1%
67 5
 
< 0.1%
65 5
 
< 0.1%
64 5
 
< 0.1%
63 5
 
< 0.1%
62 5
 
< 0.1%
61 14
0.1%

wh_est_year
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)0.2%
Missing11881
Missing (%)47.5%
Infinite0
Infinite (%)0.0%
Mean2009.3832
Minimum1996
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-03-17T14:46:42.101256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1996
5-th percentile1998
Q12003
median2009
Q32016
95-th percentile2021
Maximum2023
Range27
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.5282298
Coefficient of variation (CV)0.0037465377
Kurtosis-1.175888
Mean2009.3832
Median Absolute Deviation (MAD)7
Skewness0.012416975
Sum26361098
Variance56.674244
MonotonicityNot monotonic
2023-03-17T14:46:42.193206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2000 551
 
2.2%
2006 541
 
2.2%
2002 533
 
2.1%
2014 531
 
2.1%
2007 528
 
2.1%
2001 518
 
2.1%
2004 515
 
2.1%
2008 509
 
2.0%
2016 507
 
2.0%
1998 507
 
2.0%
Other values (18) 7879
31.5%
(Missing) 11881
47.5%
ValueCountFrequency (%)
1996 191
 
0.8%
1997 329
1.3%
1998 507
2.0%
1999 482
1.9%
2000 551
2.2%
2001 518
2.1%
2002 533
2.1%
2003 469
1.9%
2004 515
2.1%
2005 489
2.0%
ValueCountFrequency (%)
2023 142
 
0.6%
2022 332
1.3%
2021 485
1.9%
2020 496
2.0%
2019 507
2.0%
2018 498
2.0%
2017 488
2.0%
2016 507
2.0%
2015 502
2.0%
2014 531
2.1%

storage_issue_reported_l3m
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.13044
Minimum0
Maximum39
Zeros908
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-03-17T14:46:42.286933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q110
median18
Q324
95-th percentile33
Maximum39
Range39
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.1611081
Coefficient of variation (CV)0.53478534
Kurtosis-0.6801423
Mean17.13044
Median Absolute Deviation (MAD)7
Skewness0.11334521
Sum428261
Variance83.925902
MonotonicityNot monotonic
2023-03-17T14:46:42.390318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
24 1424
 
5.7%
5 1351
 
5.4%
25 1262
 
5.0%
4 1081
 
4.3%
18 1070
 
4.3%
20 1065
 
4.3%
6 1056
 
4.2%
19 1022
 
4.1%
16 938
 
3.8%
23 917
 
3.7%
Other values (27) 13814
55.3%
ValueCountFrequency (%)
0 908
3.6%
4 1081
4.3%
5 1351
5.4%
6 1056
4.2%
7 491
 
2.0%
8 406
 
1.6%
9 787
3.1%
10 637
2.5%
11 867
3.5%
12 739
3.0%
ValueCountFrequency (%)
39 156
0.6%
38 181
0.7%
37 141
0.6%
36 161
0.6%
35 181
0.7%
34 288
1.2%
33 295
1.2%
32 296
1.2%
31 289
1.2%
30 337
1.3%

temp_reg_mach
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
0
17418 
1
7582 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 17418
69.7%
1 7582
30.3%

Length

2023-03-17T14:46:42.484077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T14:46:42.562184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 17418
69.7%
1 7582
30.3%

Most occurring characters

ValueCountFrequency (%)
0 17418
69.7%
1 7582
30.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17418
69.7%
1 7582
30.3%

Most occurring scripts

ValueCountFrequency (%)
Common 25000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17418
69.7%
1 7582
30.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17418
69.7%
1 7582
30.3%
Distinct5
Distinct (%)< 0.1%
Missing908
Missing (%)3.6%
Memory size195.4 KiB
C
5501 
B+
4917 
B
4812 
A
4671 
A+
4191 

Length

Max length2
Median length1
Mean length1.3780508
Min length1

Characters and Unicode

Total characters33200
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA+
5th rowC

Common Values

ValueCountFrequency (%)
C 5501
22.0%
B+ 4917
19.7%
B 4812
19.2%
A 4671
18.7%
A+ 4191
16.8%
(Missing) 908
 
3.6%

Length

2023-03-17T14:46:42.640292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-17T14:46:42.734019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
b 9729
40.4%
a 8862
36.8%
c 5501
22.8%

Most occurring characters

ValueCountFrequency (%)
B 9729
29.3%
+ 9108
27.4%
A 8862
26.7%
C 5501
16.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 24092
72.6%
Math Symbol 9108
 
27.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 9729
40.4%
A 8862
36.8%
C 5501
22.8%
Math Symbol
ValueCountFrequency (%)
+ 9108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24092
72.6%
Common 9108
 
27.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 9729
40.4%
A 8862
36.8%
C 5501
22.8%
Common
ValueCountFrequency (%)
+ 9108
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 9729
29.3%
+ 9108
27.4%
A 8862
26.7%
C 5501
16.6%

wh_breakdown_l3m
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.48204
Minimum0
Maximum6
Zeros908
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-03-17T14:46:42.796506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6903348
Coefficient of variation (CV)0.48544382
Kurtosis-0.95214878
Mean3.48204
Median Absolute Deviation (MAD)1
Skewness-0.06802568
Sum87051
Variance2.8572317
MonotonicityNot monotonic
2023-03-17T14:46:42.874611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 5076
20.3%
3 5006
20.0%
4 4037
16.1%
6 4012
16.0%
5 3925
15.7%
1 2036
8.1%
0 908
 
3.6%
ValueCountFrequency (%)
0 908
 
3.6%
1 2036
8.1%
2 5076
20.3%
3 5006
20.0%
4 4037
16.1%
5 3925
15.7%
6 4012
16.0%
ValueCountFrequency (%)
6 4012
16.0%
5 3925
15.7%
4 4037
16.1%
3 5006
20.0%
2 5076
20.3%
1 2036
8.1%
0 908
 
3.6%

govt_check_l3m
Real number (ℝ)

Distinct32
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.81228
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-03-17T14:46:42.952716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median21
Q326
95-th percentile31
Maximum32
Range31
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6323822
Coefficient of variation (CV)0.45886953
Kurtosis-1.0573419
Mean18.81228
Median Absolute Deviation (MAD)7
Skewness-0.36326153
Sum470307
Variance74.518022
MonotonicityNot monotonic
2023-03-17T14:46:43.046446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
26 2908
 
11.6%
23 1828
 
7.3%
19 1604
 
6.4%
28 1465
 
5.9%
14 1429
 
5.7%
27 1277
 
5.1%
6 1224
 
4.9%
11 1160
 
4.6%
12 947
 
3.8%
32 940
 
3.8%
Other values (22) 10218
40.9%
ValueCountFrequency (%)
1 550
2.2%
2 431
 
1.7%
3 438
 
1.8%
4 99
 
0.4%
5 250
 
1.0%
6 1224
4.9%
7 65
 
0.3%
8 276
 
1.1%
9 932
3.7%
10 899
3.6%
ValueCountFrequency (%)
32 940
 
3.8%
31 362
 
1.4%
30 404
 
1.6%
29 901
 
3.6%
28 1465
5.9%
27 1277
5.1%
26 2908
11.6%
25 884
 
3.5%
24 628
 
2.5%
23 1828
7.3%

product_wg_ton
Real number (ℝ)

Distinct4561
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22102.633
Minimum2065
Maximum55151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-03-17T14:46:43.155790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2065
5-th percentile5128
Q113059
median22101
Q330103
95-th percentile43113
Maximum55151
Range53086
Interquartile range (IQR)17044

Descriptive statistics

Standard deviation11607.755
Coefficient of variation (CV)0.52517522
Kurtosis-0.5020222
Mean22102.633
Median Absolute Deviation (MAD)8959
Skewness0.33163104
Sum5.5256582 × 108
Variance1.3473998 × 108
MonotonicityNot monotonic
2023-03-17T14:46:43.265146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6081 21
 
0.1%
5146 21
 
0.1%
6057 21
 
0.1%
6104 20
 
0.1%
6099 20
 
0.1%
6083 20
 
0.1%
6107 19
 
0.1%
6066 19
 
0.1%
31126 19
 
0.1%
6097 19
 
0.1%
Other values (4551) 24801
99.2%
ValueCountFrequency (%)
2065 1
< 0.1%
2083 1
< 0.1%
2093 1
< 0.1%
2103 1
< 0.1%
2104 1
< 0.1%
2106 1
< 0.1%
2109 1
< 0.1%
2118 1
< 0.1%
2122 1
< 0.1%
2133 1
< 0.1%
ValueCountFrequency (%)
55151 1
< 0.1%
55150 1
< 0.1%
55144 1
< 0.1%
55132 1
< 0.1%
55120 1
< 0.1%
55115 1
< 0.1%
55112 1
< 0.1%
55111 1
< 0.1%
55095 1
< 0.1%
55093 1
< 0.1%

Interactions

2023-03-17T14:46:36.955652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:23.643398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:24.924615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:26.061791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:27.227595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:28.471916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:29.638617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:30.762964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:32.012538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:33.150035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:34.414879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:35.603115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:37.068968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:23.763036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:25.027664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:26.173300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:27.326082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:28.582983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:29.734315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:30.865768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:32.108577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:33.255607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:34.520951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:35.721514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:37.172352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:23.860252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:25.118493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:26.262871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:27.421993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:28.682723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:29.832414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:30.942446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:32.207128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:33.352763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:34.616054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:35.825087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:37.281455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:23.958703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:25.215146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:26.366645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:27.617264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:28.779931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:29.923813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:31.051311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:32.304304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:33.456960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:34.713137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:35.923380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:37.380902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:24.055790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:25.305750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:26.458149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:27.714895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:28.867620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:30.018726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:31.142635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:32.393254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:33.569953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:34.803598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:36.017580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:37.476691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:24.152426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:25.395926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:26.554106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:27.803887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:28.963353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:30.110336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:31.233759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:32.484844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:33.671788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:34.900656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:36.112895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:37.571882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:24.250051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:25.489896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:26.647435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:27.902133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:29.059593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:30.201258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:31.326042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:32.577812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:33.775541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:34.996849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:36.368698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:37.661132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:24.342366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:25.578127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:26.735688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:27.984406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:29.147892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:30.282529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:31.528651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:32.665425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:33.878949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:35.088603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:36.463956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:37.754715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:24.521314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:25.679275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:26.825801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:28.082932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:29.242691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:30.381787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:31.623396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:32.761225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:33.990059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:35.186408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:36.560097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:37.858580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:24.624519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:25.778070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:26.922870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:28.186738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:29.339052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:30.483887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:31.734967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:32.865423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:34.104370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:35.289779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:36.664357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:37.955696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:24.729359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:25.879688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:27.026828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:28.284217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:29.436142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:30.575760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:31.834933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:32.959646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:34.206818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:35.395195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:36.761374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:38.053360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:24.826185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:25.970875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:27.131649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:28.381440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:29.541571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:30.673395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:31.914644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:33.055968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:34.310860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:35.499415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-17T14:46:36.861721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-03-17T14:46:43.390115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
num_refill_req_l3mtransport_issue_l1yCompetitor_in_mktretail_shop_numdistributor_numdist_from_hubworkers_numwh_est_yearstorage_issue_reported_l3mwh_breakdown_l3mgovt_check_l3mproduct_wg_tonLocation_typeWH_capacity_sizezoneWH_regional_zonewh_owner_typeflood_impactedflood_proofelectric_supplytemp_reg_machapproved_wh_govt_certificate
num_refill_req_l3m1.0000.0160.001-0.0020.004-0.000-0.0140.016-0.006-0.000-0.0030.0030.0210.0080.0080.0080.0000.0120.0000.0180.3170.000
transport_issue_l1y0.0161.000-0.001-0.0030.0050.014-0.007-0.014-0.1430.0170.008-0.1690.0060.0110.0060.0000.0000.0000.0000.0050.0260.011
Competitor_in_mkt0.001-0.0011.000-0.1800.0000.009-0.001-0.0130.0100.011-0.0560.0100.0000.0340.3080.0490.0000.0000.0000.0000.0140.000
retail_shop_num-0.002-0.003-0.1801.000-0.000-0.002-0.0010.007-0.007-0.0110.065-0.0080.0070.0590.0510.0450.0000.0000.0000.0000.0000.004
distributor_num0.0040.0050.000-0.0001.000-0.012-0.012-0.0120.0040.005-0.0070.0050.0000.0030.0140.0000.0000.0050.0170.0090.0180.000
dist_from_hub-0.0000.0140.009-0.002-0.0121.000-0.0210.009-0.005-0.000-0.001-0.0050.0000.0000.0000.0000.0000.0180.0000.0000.0000.007
workers_num-0.014-0.007-0.001-0.001-0.012-0.0211.0000.005-0.010-0.014-0.006-0.0100.0000.0000.0000.0060.2460.1710.0980.4000.0110.000
wh_est_year0.016-0.014-0.0130.007-0.0120.0090.0051.000-0.872-0.3800.007-0.8500.1000.0160.0150.0130.0000.0000.0000.0220.1050.109
storage_issue_reported_l3m-0.006-0.1430.010-0.0070.004-0.005-0.010-0.8721.0000.350-0.0090.9890.0930.0110.0100.0080.0000.0000.0050.0170.1300.096
wh_breakdown_l3m-0.0000.0170.011-0.0110.005-0.000-0.014-0.3800.3501.000-0.0150.3390.0650.0000.0080.0000.0100.0000.0140.0000.1230.049
govt_check_l3m-0.0030.008-0.0560.065-0.007-0.001-0.0060.007-0.009-0.0151.000-0.0090.0100.6490.2840.5670.0100.0000.0050.0100.0000.000
product_wg_ton0.003-0.1690.010-0.0080.005-0.005-0.010-0.8500.9890.339-0.0091.0000.0850.0140.0000.0040.0000.0000.0000.0130.1190.127
Location_type0.0210.0060.0000.0070.0000.0000.0000.1000.0930.0650.0100.0851.0000.0110.0090.0100.0000.0000.0000.0000.0210.016
WH_capacity_size0.0080.0110.0340.0590.0030.0000.0000.0160.0110.0000.6490.0140.0111.0000.1740.8470.0000.0000.0000.0080.0000.000
zone0.0080.0060.3080.0510.0140.0000.0000.0150.0100.0080.2840.0000.0090.1741.0000.1780.0000.0110.0000.0000.0000.000
WH_regional_zone0.0080.0000.0490.0450.0000.0000.0060.0130.0080.0000.5670.0040.0100.8470.1781.0000.0000.0080.0060.0110.0120.003
wh_owner_type0.0000.0000.0000.0000.0000.0000.2460.0000.0000.0100.0100.0000.0000.0000.0000.0001.0000.1080.0290.2300.0000.000
flood_impacted0.0120.0000.0000.0000.0050.0180.1710.0000.0000.0000.0000.0000.0000.0000.0110.0080.1081.0000.1070.1650.0060.000
flood_proof0.0000.0000.0000.0000.0170.0000.0980.0000.0050.0140.0050.0000.0000.0000.0000.0060.0290.1071.0000.1140.0000.008
electric_supply0.0180.0050.0000.0000.0090.0000.4000.0220.0170.0000.0100.0130.0000.0080.0000.0110.2300.1650.1141.0000.0040.000
temp_reg_mach0.3170.0260.0140.0000.0180.0000.0110.1050.1300.1230.0000.1190.0210.0000.0000.0120.0000.0060.0000.0041.0000.445
approved_wh_govt_certificate0.0000.0110.0000.0040.0000.0070.0000.1090.0960.0490.0000.1270.0160.0000.0000.0030.0000.0000.0080.0000.4451.000

Missing values

2023-03-17T14:46:38.219640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-17T14:46:38.579536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-17T14:46:38.813336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Ware_house_IDWH_Manager_IDLocation_typeWH_capacity_sizezoneWH_regional_zonenum_refill_req_l3mtransport_issue_l1yCompetitor_in_mktretail_shop_numwh_owner_typedistributor_numflood_impactedflood_proofelectric_supplydist_from_hubworkers_numwh_est_yearstorage_issue_reported_l3mtemp_reg_machapproved_wh_govt_certificatewh_breakdown_l3mgovt_check_l3mproduct_wg_ton
0WH_100000EID_50000UrbanSmallWestZone 63124651Rented240119129.0NaN130A51517115
1WH_100001EID_50001RuralLargeNorthZone 50046217Company Owned4700121031.0NaN40A3175074
2WH_100002EID_50002RuralMidSouthZone 21044306Company Owned6400016137.0NaN170A62223137
3WH_100003EID_50003RuralMidNorthZone 37426000Rented5000010321.0NaN171A+32722115
4WH_100004EID_50004RuralLargeNorthZone 53124740Company Owned4210111225.02009.0180C62424071
5WH_100005EID_50005RuralSmallWestZone 18025053Rented3700115235.02009.0231A+3332134
6WH_100006EID_50006RuralLargeWestZone 68044449Company Owned380017727.02010.0240B3630142
7WH_100007EID_50007RuralLargeNorthZone 51047183Rented4500024123.0NaN180C62424093
8WH_100008EID_50008RuralSmallSouthZone 68145381Rented4200112422.02013.0131A+5218082
9WH_100009EID_50009RuralSmallSouthZone 64333869Company Owned350007843.0NaN60C627130
Ware_house_IDWH_Manager_IDLocation_typeWH_capacity_sizezoneWH_regional_zonenum_refill_req_l3mtransport_issue_l1yCompetitor_in_mktretail_shop_numwh_owner_typedistributor_numflood_impactedflood_proofelectric_supplydist_from_hubworkers_numwh_est_yearstorage_issue_reported_l3mtemp_reg_machapproved_wh_govt_certificatewh_breakdown_l3mgovt_check_l3mproduct_wg_ton
24990WH_124990EID_74990RuralSmallSouthZone 13034124Rented190009827.0NaN261A+32134098
24991WH_124991EID_74991RuralMidWestZone 44043672Rented5801018326.02006.0281C62637065
24992WH_124992EID_74992RuralLargeWestZone 53024312Company Owned1600019020.02007.0170A41023101
24993WH_124993EID_74993RuralMidSouthZone 35024591Rented3300116333.02000.0220B+51926091
24994WH_124994EID_74994RuralMidNorthZone 47035242Rented410007125.02016.090B12611083
24995WH_124995EID_74995RuralSmallNorthZone 13045390Rented1900114234.02005.0221A23032093
24996WH_124996EID_74996RuralMidWestZone 26044490Company Owned5700113028.02012.0100B41812114
24997WH_124997EID_74997UrbanLargeSouthZone 57025403Rented31101147NaNNaN230B+52527080
24998WH_124998EID_74998RuralSmallNorthZone 110210562Rented250016025.0NaN180A63025093
24999WH_124999EID_74999RuralMidWestZone 48245664Company Owned2101123939.02019.040B+2115058